Injection Based Dynamic Power Management and a Policy for Multiprocessor Systems
|
| Title | Injection Based Dynamic Power Management and a Policy for Multiprocessor Systems |
| Authors | |
| Abstract | Power consumption has become a critical issue in designing computer systems. Dynamic power management is an approach that aims to reduce power consumption at system level by selectively placing components into low power states. Time-out and prediction based policies are often adopted in practical systems. However, they have to accurately determine the time in low power state and otherwise the saved power consumption is not worth the loss of performance. In this paper, a power management for multiprocessor systems is proposed to optimally reduce the power consumption of multiple processors. The key feature of the proposed power management is that how long to place a processor into low power state is determined in advance but not decided when a processor becomes idle. Thus, many off-time quanta are pre-determined beforehand. The proposed power management schedules the off-time quanta to processors and a processor is placed into low power state if an off-time quantum is assigned to it. It seems that processors execute special tasks which just reduce the power supplied to them. Hence, the off-time quanta are also named sleep tasks, which are virtual and injected into the original task traffic. By doing so, the inaccurate time length of sleep tasks hardly impacts on the performance, because if a processor is blocked by a sleep task there is another one available except that all the others are blocked at the same time. Then a probabilistic policy is also proposed to optimally assign sleep tasks from the waiting queue to the processors for minimum loss of performance. In the proposed policy, high priority is given to real tasks and sleep tasks are serviced only on necessity. The analysis of the probabilistic policy is performed on a queueing model and shows that the policy is asymptotically optimal. The proposed power management and policy are further examined in empirical studies. |
| Publisher | International Journal of Networking and Computing |
| Date | 2013-01-21 |
| Source | 2185-2839 |